Emotional consciousness preserved in patients with disorders of consciousness?

Increasing evidence from studies of brain responses to subject’s own name (SON) indicates that residual consciousness is preserved in patients with disorders of consciousness (DOC) and that specific network activation might provide evidence of consciousness. However, it remains unclear whether SON is suitable for detection of emotional consciousness; moreover, the particular aspects of brain network organization that are critical for consciousness are unknown. The present study used an innovative approach to explore affective consciousness in patients with DOC during emotional stimuli. EEG data were acquired from 15 patients and 15 healthy volunteers. We analyzed brain potentials and functional network connectivity with a passive emotional paradigm based on graph theoretical methods. Larger N1 or P3a was detected in patients upon exposure to emotional sound, relative to neutral stimuli. Brain topology revealed that emotional sound evoked significantly stronger network linkages in healthy controls; additionally, it evoked several connectivity changes in patients with DOC. In conclusion, emotional consciousness might be partially preserved in patients with DOC; moreover, EEG network patterns could provide new insights into the neural activity of emotional perception in these patients.

Self-prior image-guided MRI reconstruction with dictionary learning

PURPOSE

A novel method, named self-prior image-guided MRI reconstruction with dictionary learning (SPIDLE), is developed to improve the performance of MR imaging with high acceleration rates. "self-prior" means that the prior image is obtained from the target image itself and any extra MRI scans are not needed.

METHODS

The proposed method integrates self-prior image constraint with compressed sensing (CS) and the dictionary learning (DL) technique. The self-prior image is a preliminary result reconstructed using the undersampled k-space measurements of the target image. Therefore, the self-prior image has similar structural features with the target image, and they match each other accurately. CS approach is applied to the residual error of the target image with the self-prior image, because the error image is much sparser than the target image. The split Bregman method is used to solve the proposed approach to promote fast convergence. For multicoil measurements, each coil image is reconstructed individually and the final result is produced as the square root of sum of squares (SOS) of all channel images.

RESULTS

The performance of the proposed SPIDLE method was inspected using different undersampling schemes and acceleration rates with various types of in vivo MR datasets. Experiments showed that the SPIDLE method is superior to other typical state-of-the-art methods. Specifically, the SPIDLE method produces fewer reconstruction errors, and it is robust to initialization.

CONCLUSIONS

The proposed SPIDLE method substantially widens the applications of prior image-guided MRI reconstruction, especially for applications that are not suitable to use existing MR scans as prior images. The SPIDLE method obviously improves the reconstruction quality for highly undersampled MRI. It is also promising for reconstruction of dynamic MRI and other imaging modalities, such as CT and CT-MRI multimodality imaging.

EEG patterns of self-paced movement imaginations towards externally-cued and internally-selected targets

In this study, we investigate the neurophysiological signature of the interacting processes which lead to a single reach-and-grasp movement imagination (MI). While performing this task, the human healthy participants could either define their movement targets according to an external cue, or through an internal selection process. After defining their target, they could start the MI whenever they wanted. We recorded high density electroencephalographic (EEG) activity and investigated two neural correlates: the event-related potentials (ERPs) associated with the target selection, which reflect the perceptual and cognitive processes prior to the MI, and the movement-related cortical potentials (MRCPs), associated with the planning of the self-paced MI. We found differences in frontal and parietal areas between the late ERP components related to the internally-driven selection and the externally-cued process. Furthermore, we could reliably estimate the MI onset of the self-paced task. Next, we extracted MRCP features around the MI onset to train classifiers of movement vs. rest directly on self-paced MI data. We attained performance significantly higher than chance level for both time-locked and asynchronous classification. These findings contribute to the development of more intuitive brain-computer interfaces in which movement targets are defined internally and the movements are self-paced.

Patient-specific model-based segmentation of brain tumors in 3D intraoperative ultrasound images

PURPOSE

Intraoperative ultrasound (iUS) imaging is commonly used to support brain tumor operation. The tumor segmentation in the iUS images is a difficult task and still under improvement because of the low signal-to-noise ratio. The success of automatic methods is also limited due to the high noise sensibility. Therefore, an alternative brain tumor segmentation method in 3D-iUS data using a tumor model obtained from magnetic resonance (MR) data for local MR-iUS registration is presented in this paper. The aim is to enhance the visualization of the brain tumor contours in iUS.

METHODS

A multistep approach is proposed. First, a region of interest (ROI) based on the specific patient tumor model is defined. Second, hyperechogenic structures, mainly tumor tissues, are extracted from the ROI of both modalities by using automatic thresholding techniques. Third, the registration is performed over the extracted binary sub-volumes using a similarity measure based on gradient values, and rigid and affine transformations. Finally, the tumor model is aligned with the 3D-iUS data, and its contours are represented.

RESULTS

Experiments were successfully conducted on a dataset of 33 patients. The method was evaluated by comparing the tumor segmentation with expert manual delineations using two binary metrics: contour mean distance and Dice index. The proposed segmentation method using local and binary registration was compared with two grayscale-based approaches. The outcomes showed that our approach reached better results in terms of computational time and accuracy than the comparative methods.

CONCLUSION

The proposed approach requires limited interaction and reduced computation time, making it relevant for intraoperative use. Experimental results and evaluations were performed offline. The developed tool could be useful for brain tumor resection supporting neurosurgeons to improve tumor border visualization in the iUS volumes.

Real-time MRI guidance of cardiac interventions

Cardiac magnetic resonance imaging (MRI) is appealing to guide complex cardiac procedures because it is ionizing radiation-free and offers flexible soft-tissue contrast. Interventional cardiac MR promises to improve existing procedures and enable new ones for complex arrhythmias, as well as congenital and structural heart disease. Guiding invasive procedures demands faster image acquisition, reconstruction and analysis, as well as intuitive intraprocedural display of imaging data. Standard cardiac MR techniques such as 3D anatomical imaging, cardiac function and flow, parameter mapping, and late-gadolinium enhancement can be used to gather valuable clinical data at various procedural stages. Rapid intraprocedural image analysis can extract and highlight critical information about interventional targets and outcomes. In some cases, real-time interactive imaging is used to provide a continuous stream of images displayed to interventionalists for dynamic device navigation. Alternatively, devices are navigated relative to a roadmap of major cardiac structures generated through fast segmentation and registration. Interventional devices can be visualized and tracked throughout a procedure with specialized imaging methods. In a clinical setting, advanced imaging must be integrated with other clinical tools and patient data. In order to perform these complex procedures, interventional cardiac MR relies on customized equipment, such as interactive imaging environments, in-room image display, audio communication, hemodynamic monitoring and recording systems, and electroanatomical mapping and ablation systems. Operating in this sophisticated environment requires coordination and planning. This review provides an overview of the imaging technology used in MRI-guided cardiac interventions. Specifically, this review outlines clinical targets, standard image acquisition and analysis tools, and the integration of these tools into clinical workflow.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017;46:935-950.

GPU accelerated dynamic respiratory motion model correction for MRI-guided cardiac interventions

BACKGROUND AND OBJECTIVES

The use of pre-procedural magnetic resonance (MR) roadmap images for interventional guidance has limited anatomical accuracy due to intra-procedural respiratory motion of the heart. Therefore, the objective of this study is to explore the use of a rapidly updated dynamic motion model to correct for respiratory motion induced errors during MRI-guided cardiac interventions. The motivation for the proposed technique is to improve the accuracy of MRI guidance by taking advantage of the anatomical context provided by the high resolution prior images and the respiratory motion information present in a series of realtime MR images.

METHODS

We implemented a GPU accelerated image registration algorithm to derive the respiratory motion information and used the resulting transformation parameters to update an adaptive motion model once every heart cycle. In the subsequent heart cycle, the dynamic motion model could be used to predict the respiratory motion and provide a motion estimate to realign the prior volume with the realtime MR image. This iterative update and prediction process is then continuously repeated.

RESULTS

The GPU accelerated image registration algorithm could be completed in an average of 176.9 ± 14.0 ms, which is 139× faster than a CPU implementation. Thus, it was feasible to update the dynamic model once every heart cycle. The proposed dynamic model was also able to improve the registration accuracy from 86.0 ± 7.5% to 93.0 ± 3.3% in case of variable breathing patterns, as evaluated by the dice similarity coefficient of the left ventricular border overlap between the prior and realtime images.

CONCLUSIONS

The feasibility of a dynamic motion correction framework was demonstrated. The resulting improvements may lead to more accurate MRI-guided cardiac interventions in the future.

Feasibility Study of Respiratory Motion Modeling Based Correction for MRI-Guided Intracardiac Interventional Procedures

GOAL

The purpose of this study is to improve the accuracy of interventional catheter guidance during intracardiac procedures. Specifically, the use of preprocedural magnetic resonance roadmap images for interventional guidance has limited anatomical accuracy due to intraprocedural respiratory motion of the heart. Therefore, we propose to build a novel respiratory motion model to compensate for this motion-induced error during magnetic resonance imaging (MRI)-guided procedures.

METHODS

We acquire 2-D real-time free-breathing images to characterize the respiratory motion, and build a smooth motion model via registration of 3-D prior roadmap images to the real-time images within a novel principal axes frame of reference. The model is subsequently used to correct the interventional catheter positions with respect to the anatomy of the heart.

RESULTS

We demonstrate that the proposed modeling framework can lead to smoother motion models, and potentially lead to more accurate motion estimates. Specifically, MRI-guided intracardiac ablations were performed in six preclinical animal experiments. Then, from retrospective analysis, the proposed motion modeling technique showed the potential to achieve a 27% improvement in ablation targeting accuracy.

CONCLUSION

The feasibility of a respiratory motion model-based correction framework has been successfully demonstrated.

SIGNIFICANCE

The improvement in ablation accuracy may lead to significant improvements in success rate and patient outcomes for MRI-guided intracardiac procedures.

A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes

QR bar codes are prototypical images for which part of the image is a priori known (required patterns). Open source bar code readers, such as ZBar, are readily available. We exploit both these facts to provide and assess purely regularization-based methods for blind deblurring of QR bar codes in the presence of noise.